Rain-on-snow events in headwater catchments often cause catastrophic downstream floods. Other times, large rainfall events over snow fail to trigger even slight snowmelt, thus making accurate streamflow prediction extremely important for reservoir managers. Previous studies have concluded that atmospheric condensation within the snowpack is the primary mechanism responsible for melt during rain-on-snow events. Therefore, the complex combination of snowpack state conditions prior to the storm and meteorological conditions during the storm influence melt rates and subsequent runoff amounts. This study examines known historical rain-on-snow storm events to determine potential causes of flooding from a physical process perspective using the iSnobal distributed energy balance snow model. Perturbations of initial snowpack energetic conditions, represented by the modeled cold content, and meteorological conditions such as air temperature, wind speed, and precipitation mass enable an ensemble modeling approach, which is then implemented in a cluster computing environment. Results demonstrate that prior knowledge of the distributed snowpack mass and energetics from a physically based model combined with accurate model forcing data from weather forecasts can inform future decisions about reservoir releases and help avert potential flooding disasters.